install.packages("rdbnomics")
library(rdbnomics)Fetching and Visualizing Official Statistics with R
Interfaces to Official Statistics
- Packages or set of classes and methods to read data and metadata documents through exchange frameworks
- pxweb: Access to data sources using the PX-Web API (e.g. Statistics Sweden, Statistics Estonia)
DBnomics
- DBnomics is a database of databases
- free platform to aggregate publicly-available economic data provided by national and international statistical institutions, but also by researchers and private companies
- Unified interface to access data from many sources
- Harmonized data formats and metadata
- Data series are available upon release by the provider
- Each revision is archived to build a real-time database
How to fetch data (from DBnomics using R)
- DBnomics R client
Packages used in this tutorial
- 📦 Fetching data (
rdbnomics) - 🧹 Data wrangling and transformation (
tidyverse) - 📊 Visualization (
ggplot2,plotly) - 📋 Tabular summaries (
gt) - 🧾 Building this presentation (
quarto)
library(quarto) # for compiling Quarto presentations
library(rdbnomics) # for accessing economic data via DBnomics
library(tidyverse) # dplyr, ggplot2, readr, etc.
library(plotly) # interactive visualizations
library(gt) # pretty tablesExample: Fetch Unemployment Data
- Assume we know exactly the series ID we want to fetch
- Unemployment rate, ILO definition, total, Estonia, from Eurostat
unemp <- rdb(ids = "Eurostat/ei_lmhr_m/M.PC_ACT.SA.LM-UN-T-TOT.EE") # fetch dataglimpse(unemp)Rows: 296
Columns: 22
$ `@frequency` <chr> "monthly", "monthly", "monthly", "mo…
$ dataset_code <chr> "ei_lmhr_m", "ei_lmhr_m", "ei_lmhr_m…
$ dataset_name <chr> "Unemployment rate (%) - monthly dat…
$ freq <chr> "M", "M", "M", "M", "M", "M", "M", "…
$ geo <chr> "EE", "EE", "EE", "EE", "EE", "EE", …
$ `Geopolitical entity (reporting)` <chr> "Estonia", "Estonia", "Estonia", "Es…
$ indexed_at <dttm> 2024-10-31 15:26:51, 2024-10-31 15:…
$ indic <chr> "LM-UN-T-TOT", "LM-UN-T-TOT", "LM-UN…
$ Indicator <chr> "Unemployment according to ILO defin…
$ observations_attributes <chr> "OBS_FLAG,", "OBS_FLAG,", "OBS_FLAG,…
$ original_period <chr> "2000-02", "2000-03", "2000-04", "20…
$ original_value <chr> "14.9", "14.2", "14.5", "13.9", "14"…
$ period <date> 2000-02-01, 2000-03-01, 2000-04-01,…
$ provider_code <chr> "Eurostat", "Eurostat", "Eurostat", …
$ s_adj <chr> "SA", "SA", "SA", "SA", "SA", "SA", …
$ `Seasonal adjustment` <chr> "Seasonally adjusted data, not calen…
$ series_code <chr> "M.PC_ACT.SA.LM-UN-T-TOT.EE", "M.PC_…
$ series_name <chr> "Monthly – Percentage of population …
$ `Time frequency` <chr> "Monthly", "Monthly", "Monthly", "Mo…
$ unit <chr> "PC_ACT", "PC_ACT", "PC_ACT", "PC_AC…
$ `Unit of measure` <chr> "Percentage of population in the lab…
$ value <dbl> 14.9, 14.2, 14.5, 13.9, 14.0, 13.9, …colnames(unemp) [1] "@frequency" "dataset_code"
[3] "dataset_name" "freq"
[5] "geo" "Geopolitical entity (reporting)"
[7] "indexed_at" "indic"
[9] "Indicator" "observations_attributes"
[11] "original_period" "original_value"
[13] "period" "provider_code"
[15] "s_adj" "Seasonal adjustment"
[17] "series_code" "series_name"
[19] "Time frequency" "unit"
[21] "Unit of measure" "value" # Extract source and series ID from the metadata
(source_name <- unique(unemp$dataset_code)) [1] "ei_lmhr_m"(provider_code <- unique(unemp$provider_code)) [1] "Eurostat"(country_name <- unique(unemp$`Geopolitical entity (reporting)`) )[1] "Estonia"(series_id <- unique(unemp$series_code)) [1] "M.PC_ACT.SA.LM-UN-T-TOT.EE"# Plot the data
p1 <- ggplot(unemp, aes(x = period, y = value)) +
geom_line(color = "steelblue", linewidth = 1) +
labs(
title = paste("Unemployment Rate in ", country_name),
subtitle = paste("Monthly, seasonally adjusted —", provider_code),
x = "Date", y = "Percent",
caption = paste("Source:", provider_code, "| Dataset:", source_name, "| ID:", series_id)
) +
theme_minimal()
p1
Interactive plot
ggplotly(p1)How do we find the series ID/mask/dimensions?
- Go to the DBnomics website
- Show the available datasets of a provider:
head(rdb_datasets(provider_code = "Eurostat"))$Eurostat
code
<char>
1: aact_ali01
2: aact_ali02
3: aact_eaa01
4: aact_eaa02
5: aact_eaa03
---
8289: yth_empl_120
8290: yth_empl_130
8291: yth_empl_130
8292: yth_empl_140
8293: yth_empl_140
name
<char>
1: Agricultural labour input statistics: absolute figures (1 000 annual work units)
2: Agricultural labour input statistics: indices
3: Economic accounts for agriculture - values at current prices
4: Economic accounts for agriculture - values at n-1 prices
5: Economic accounts for agriculture - values at constant prices (2015 = 100)
---
8289: Youth long-term unemployment rate (12 months or longer) by sex and age
8290: Youth long-term unemployment rate (12 months or longer) by sex, age and NUTS 2 regions
8291: Youth long-term unemployment rate (12 months or longer) by sex, age and NUTS 2 region
8292: Youth unemployment ratio by sex, age and NUTS 2 regions
8293: Youth unemployment ratio by sex, age and NUTS 2 region- Show the dimensions of a dataset:
head(rdb_dimensions(provider_code = "Eurostat", dataset_code = "ei_lmhr_m"))$Eurostat
$Eurostat$ei_lmhr_m
$Eurostat$ei_lmhr_m$freq
freq Time frequency
<char> <char>
1: M Monthly
$Eurostat$ei_lmhr_m$geo
geo Geopolitical entity (reporting)
<char> <char>
1: AT Austria
2: BA Bosnia and Herzegovina
3: BE Belgium
4: BG Bulgaria
5: CH Switzerland
6: CY Cyprus
7: CZ Czechia
8: DE Germany
9: DK Denmark
10: EA20 Euro area – 20 countries (from 2023)
11: EE Estonia
12: EL Greece
13: ES Spain
14: EU27_2020 European Union - 27 countries (from 2020)
15: FI Finland
16: FR France
17: HR Croatia
18: HU Hungary
19: IE Ireland
20: IS Iceland
21: IT Italy
22: JP Japan
23: LT Lithuania
24: LU Luxembourg
25: LV Latvia
26: MT Malta
27: NL Netherlands
28: NO Norway
29: PL Poland
30: PT Portugal
31: RO Romania
32: SE Sweden
33: SI Slovenia
34: SK Slovakia
35: TR Türkiye
36: UK United Kingdom
37: US United States
geo Geopolitical entity (reporting)
$Eurostat$ei_lmhr_m$indic
indic
<char>
1: LM-UN-F-GT25
2: LM-UN-F-LE25
3: LM-UN-F-TOT
4: LM-UN-M-GT25
5: LM-UN-M-LE25
6: LM-UN-M-TOT
7: LM-UN-T-GT25
8: LM-UN-T-LE25
9: LM-UN-T-TOT
Indicator
<char>
1: Unemployment according to ILO definition - over 25 years - females
2: Unemployment according to ILO definition - under 25 years - females
3: Unemployment according to ILO definition - females
4: Unemployment according to ILO definition - over 25 years - males
5: Unemployment according to ILO definition - under 25 years - males
6: Unemployment according to ILO definition - males
7: Unemployment according to ILO definition - over 25 years - total
8: Unemployment according to ILO definition - under 25 years - total
9: Unemployment according to ILO definition - total
$Eurostat$ei_lmhr_m$s_adj
s_adj
<char>
1: NSA
2: SA
Seasonal adjustment
<char>
1: Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data)
2: Seasonally adjusted data, not calendar adjusted data
$Eurostat$ei_lmhr_m$unit
unit Unit of measure
<char> <char>
1: PC_ACT Percentage of population in the labour force- Query to filter/select series from a provider’s dataset
head(rdb_series(
provider = "Eurostat",
dataset_code = "ei_lmhr_m",
query = "United Kingdom"
))$Eurostat
$Eurostat$ei_lmhr_m
series_code
<char>
1: M.PC_ACT.NSA.LM-UN-F-GT25.UK
2: M.PC_ACT.NSA.LM-UN-F-LE25.UK
3: M.PC_ACT.NSA.LM-UN-F-TOT.UK
4: M.PC_ACT.NSA.LM-UN-M-GT25.UK
5: M.PC_ACT.NSA.LM-UN-M-LE25.UK
6: M.PC_ACT.NSA.LM-UN-M-TOT.UK
7: M.PC_ACT.NSA.LM-UN-T-GT25.UK
8: M.PC_ACT.NSA.LM-UN-T-LE25.UK
9: M.PC_ACT.NSA.LM-UN-T-TOT.UK
10: M.PC_ACT.SA.LM-UN-F-GT25.UK
11: M.PC_ACT.SA.LM-UN-F-LE25.UK
12: M.PC_ACT.SA.LM-UN-F-TOT.UK
13: M.PC_ACT.SA.LM-UN-M-GT25.UK
14: M.PC_ACT.SA.LM-UN-M-LE25.UK
15: M.PC_ACT.SA.LM-UN-M-TOT.UK
16: M.PC_ACT.SA.LM-UN-T-GT25.UK
17: M.PC_ACT.SA.LM-UN-T-LE25.UK
18: M.PC_ACT.SA.LM-UN-T-TOT.UK
series_name
<char>
1: Monthly – Percentage of population in the labour force – Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) – Unemployment according to ILO definition - over 25 years - females – United Kingdom
2: Monthly – Percentage of population in the labour force – Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) – Unemployment according to ILO definition - under 25 years - females – United Kingdom
3: Monthly – Percentage of population in the labour force – Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) – Unemployment according to ILO definition - females – United Kingdom
4: Monthly – Percentage of population in the labour force – Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) – Unemployment according to ILO definition - over 25 years - males – United Kingdom
5: Monthly – Percentage of population in the labour force – Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) – Unemployment according to ILO definition - under 25 years - males – United Kingdom
6: Monthly – Percentage of population in the labour force – Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) – Unemployment according to ILO definition - males – United Kingdom
7: Monthly – Percentage of population in the labour force – Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) – Unemployment according to ILO definition - over 25 years - total – United Kingdom
8: Monthly – Percentage of population in the labour force – Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) – Unemployment according to ILO definition - under 25 years - total – United Kingdom
9: Monthly – Percentage of population in the labour force – Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) – Unemployment according to ILO definition - total – United Kingdom
10: Monthly – Percentage of population in the labour force – Seasonally adjusted data, not calendar adjusted data – Unemployment according to ILO definition - over 25 years - females – United Kingdom
11: Monthly – Percentage of population in the labour force – Seasonally adjusted data, not calendar adjusted data – Unemployment according to ILO definition - under 25 years - females – United Kingdom
12: Monthly – Percentage of population in the labour force – Seasonally adjusted data, not calendar adjusted data – Unemployment according to ILO definition - females – United Kingdom
13: Monthly – Percentage of population in the labour force – Seasonally adjusted data, not calendar adjusted data – Unemployment according to ILO definition - over 25 years - males – United Kingdom
14: Monthly – Percentage of population in the labour force – Seasonally adjusted data, not calendar adjusted data – Unemployment according to ILO definition - under 25 years - males – United Kingdom
15: Monthly – Percentage of population in the labour force – Seasonally adjusted data, not calendar adjusted data – Unemployment according to ILO definition - males – United Kingdom
16: Monthly – Percentage of population in the labour force – Seasonally adjusted data, not calendar adjusted data – Unemployment according to ILO definition - over 25 years - total – United Kingdom
17: Monthly – Percentage of population in the labour force – Seasonally adjusted data, not calendar adjusted data – Unemployment according to ILO definition - under 25 years - total – United Kingdom
18: Monthly – Percentage of population in the labour force – Seasonally adjusted data, not calendar adjusted data – Unemployment according to ILO definition - total – United KingdomFetch two (or more) series at once
- Example: Balance of Payments (BOP) for France and Germany from the IMF for Current Account, Total, Net, Euros, Millions, Annual
# by ID
bop <- rdb(ids = c("IMF/BOP/A.FR.BCA_BP6_EUR", "IMF/BOP/A.DE.BCA_BP6_EUR"))
bop %>% count(`Reference Area`) Reference Area n
<char> <int>
1: France 15
2: Germany 26# by Mask
bop <- rdb(provider = "IMF",
dataset_code = "BOP",
mask = "A.FR+DE.BCA_BP6_EUR")
bop %>% count(`Reference Area`) Reference Area n
<char> <int>
1: France 15
2: Germany 26⚠You can not specify a dimension without a value!
# by Dimension
dim <- list(
REF_AREA = c("DE", "FR"),
INDICATOR = c("BCA_BP6_EUR"),
FREQ = "A"
)
## Here I do not include FREQUENCY in the dimension list. I would download annual and quarterly data
# dim <- list(
# REF_AREA = c("DE", "FR"),
# INDICATOR = c("BCA_BP6_EUR")
# )
bop <- rdb(provider = "IMF", dataset_code = "BOP", dimensions = dim)
bop %>% count(`Reference Area`) Reference Area n
<char> <int>
1: France 15
2: Germany 26# Line plot with color by country
p2 <- ggplot(bop, aes(x = period, y = value, color = `Reference Area`)) +
geom_step(linewidth = 1) +
labs(
title = "Balance of Payments (BCA, EUR)",
subtitle = "France vs Germany — Annual",
x = "Year",
y = "EUR (Millions)",
caption = "Source: IMF / DBnomics"
) +
theme_minimal()
p2
Fetch two series from different datasets of different providers
unemp2 <- rdb(ids = c("AMECO/ZUTN/EA19.1.0.0.0.ZUTN", "Eurostat/une_rt_q/Q.SA.Y15-24.PC_ACT.T.EA19"))# See which providers and datasets are included
dim(unemp2)[1] 122 27unique(unemp2$provider_code)[1] "AMECO" "Eurostat"unique(unemp2$dataset_code)[1] "ZUTN" "une_rt_q"unique(unemp2$series_code)[1] "EA19.1.0.0.0.ZUTN" "Q.SA.Y15-24.PC_ACT.T.EA19"unique(unemp2$`@frequency`)[1] "annual" "quarterly"unique(unemp2$`Seasonal adjustment`)[1] NA
[2] "Seasonally adjusted data, not calendar adjusted data"# Summarize coverage and data availability
unemp2_summary <- unemp2 %>%
group_by(series_code) %>%
summarize(
provider = first(provider_code),
dataset = first(dataset_code),
start_all = min(period, na.rm = TRUE),
end_all = max(period, na.rm = TRUE),
start_data = min(period[!is.na(value)]),
end_data = max(period[!is.na(value)]),
n_obs = sum(!is.na(value)),
.groups = "drop"
)unemp2_summary_table <- unemp2_summary |>
gt() %>%
tab_header(
title = "Time Coverage and Non-Missing Observations",
subtitle = "For Each Series from AMECO and Eurostat"
) %>%
cols_label(
series_code = "Series ID",
provider = "Provider",
dataset = "Dataset",
start_all = "Start (all)",
end_all = "End (all)",
start_data = "Start (non-NA)",
end_data = "End (non-NA)",
n_obs = "# Obs"
) %>%
fmt_date(
columns = c(start_all, end_all, start_data, end_data),
date_style = "iso"
) %>%
tab_options(
table.width = pct(100),
column_labels.font.weight = "bold"
)unemp2_summary_table| Time Coverage and Non-Missing Observations | |||||||
|---|---|---|---|---|---|---|---|
| For Each Series from AMECO and Eurostat | |||||||
| Series ID | Provider | Dataset | Start (all) | End (all) | Start (non-NA) | End (non-NA) | # Obs |
| EA19.1.0.0.0.ZUTN | AMECO | ZUTN | 1960-01-01 | 2026-01-01 | 1997-01-01 | 2026-01-01 | 30 |
| Q.SA.Y15-24.PC_ACT.T.EA19 | Eurostat | une_rt_q | 2009-01-01 | 2022-07-01 | 2009-01-01 | 2022-07-01 | 55 |
# Metadata vectors
providers <- unique(unemp2$provider_code)
datasets <- unique(unemp2$dataset_code)
series_ids <- unique(unemp2$series_code)# Create a label that combines dataset + series ID
unemp2_clean <- unemp2 %>%
drop_na(value) %>%
mutate(label = case_when(
series_code == "EA19.1.0.0.0.ZUTN" ~ "Total, AMECO",
series_code == "Q.SA.Y15-24.PC_ACT.T.EA19" ~ "Youth (15–24), Eurostat",
TRUE ~ series_code
))p3 <- ggplot(unemp2_clean, aes(x = period, y = value, color = label)) +
geom_line(linewidth = 1) +
labs(
title = "Unemployment Rates from Multiple Sources (EA19)",
subtitle = "AMECO and Eurostat — Different definitions",
x = "Year", y = "Percent",
caption = paste("Series IDs:", paste(unique(unemp2_clean$series_code), collapse = " | "))
) +
theme_minimal()
p3
Fetch large amounts of data
- Sometimes you need to fetch many if not all dimensions of the data
- You can wildcard dimension and post-filter
- Example: MFI Interest Rate Statistics from the ECB
- Start with a single series (Estonia, mortgage rates)
mir_mortgage_ee <- rdb("ECB", "MIR", "M.EE.B.A2C.A.R.A.2250.EUR.N")
unique(mir_mortgage_ee$series_name)[1] "Monthly – Estonia – Deposit-taking corporations except the central bank (S.122) – Lending for house purchase excluding revolving loans and overdrafts, convenience and extended credit card debt – Total – Annualised agreed rate (AAR) / Narrowly defined effective rate (NDER) – Total – Households and non-profit institutions serving households (S.14 and S.15) – Euro – New business"Wildcarding dimensions
- To fetch multiple values for a dimension (e.g. countries), just remove the value from that position
- Example: remove
"EE"to fetch all countries (REF_AREA)
- Example: remove
⚠️ This can take a while
# mir_mortgage_ee <- rdb("ECB", "MIR", "M.EE.B.A2C.A.R.A.2250.EUR.N")
mir <- rdb("ECB", "MIR", "M..B..A.R.A..EUR.N")
dim(mir)[1] 105479 31format(object.size(mir), units = "Mb")[1] "25.3 Mb"unique(mir$REF_AREA) [1] "AT" "BE" "CY" "DE" "EE" "ES" "FI" "FR" "GR" "HR" "IE" "IT" "LT" "LU" "LV"
[16] "MT" "NL" "PT" "SI" "SK" "U2"unique(mir$BS_ITEM) [1] "A2A" "A2AC" "A2B" "A2BC" "A2C" "A2CC" "A2D" "A2Z" "A2Z1" "A2Z3"
[11] "L21" "L22" "L23" "L24" unique(mir$`BS counterpart sector`)[1] "Non-Financial corporations (S.11)"
[2] "Households and non-profit institutions serving households (S.14 and S.15)"
[3] "Households of which sole proprietors and unincorporated partnerships (SP/UP)"
[4] "Non-Financial corporations and Households (S.11 and S.14 and S.15)" Filter and plot
- Filter Estonia, Latvia and Lithuania
- Keep only selected BS items (loan categories)
# Filter by BS_ITEM and countries
mir_filtered <- mir %>%
filter(
REF_AREA %in% c("EE", "LV", "LT"),
BS_ITEM %in% c("A2I", "A2C", "A2B", "A2J", "A2A")
)Plot interest rates by country & type
country_list <- paste(sort(unique(mir_filtered$REF_AREA)), collapse = ", ")
item_list <- paste(unique(mir_filtered$BS_ITEM), collapse = ", ")
caption_text <- paste(
"Source: ECB / DBnomics — Dataset code: MIR",
paste0("\nFiltered: REF_AREA in ", country_list, "; BS_ITEM in ", item_list)
)
mir_filtered <- mir_filtered %>%
mutate(facet_label = paste0(`BS counterpart sector`, ".\n\n", `Balance sheet item`))
p4 <- ggplot(mir_filtered, aes(x = period, y = value, color = REF_AREA)) +
geom_line(linewidth = 0.8) +
facet_wrap(~ facet_label, labeller = label_wrap_gen(width = 30), ncol = 3) +
labs(
title = "Interest Rates for Households and Firms",
subtitle = "Faceted by Loan Type and Borrower Sector",
x = "Date", y = "Percent",
caption = caption_text
) +
theme_minimal() +
theme(legend.position = "bottom")
p4
How to Fetch Data from Statistics Estonia in R
- Statistics Estonia is not yet available through DBnomics.
- There is no dedicated R package for accessing data from andmed.stat.ee.
- However, Statistics Estonia provides a PX-WEB API, which can be accessed from R using the
pxwebpackage. pxwebis a general-purpose client for PX-WEB APIs used by many statistical agencies (e.g., Sweden, Finland, Estonia).
install.packages("pxweb")
library(pxweb)Accessing Statistics Estonia Data with pxweb
- Two main approaches:
pxweb_interactive()– guided interface in consolepxweb_get()– programmatic access
Interactive Access with pxweb_interactive()
Launch a guided interface for browsing and selecting data
Choose from top-level domains (e.g. Economy, Population)
Filter by category, time period, etc.
Preview results and download the dataset
Automatically generates reproducible R code
Top-level database address is:
- In Estonian: https://andmed.stat.ee/api/v1/et/stat
- In English: https://andmed.stat.ee/api/v1/en/stat
pxweb_interactive("https://andmed.stat.ee/api/v1/en/stat")
============================================================================================================================
R PXWEB: Content of 'andmed.stat.ee'
at '/api/v1/en/stat'
============================================================================================================================
[ 1 ] : Environment
[ 2 ] : Economy
[ 3 ] : Population
[ 4 ] : Social life
[ 5 ] : Multidomain statistics
[ 6 ] : Population and Housing Census
[ 7 ] : Discontinued datasets
============================================================================================================================
Enter your choice:
('esc' = Quit, 'b' = Back, 'i' = Show id)
1:
Do you want to print code to query and download data?
Enter your choice:
('esc' = Quit, 'y' = Yes, 'n' = No)
1: y
Do you want to print query in json format (otherwise query is printed as an R list)?
Enter your choice:
('esc' = Quit, 'y' = Yes, 'n' = No)
1: n
Do you want to download the data?
Enter your choice:
('esc' = Quit, 'y' = Yes, 'n' = No)
1: y
Do you want to return a the data as a data.frame?
Enter your choice:
('esc' = Quit, 'y' = Yes, 'n' = No)
1: y
Do you want to print citation for the data?
Enter your choice:
('esc' = Quit, 'y' = Yes, 'n' = No)
1: n
Direct Query with pxweb_get()
- Use when you already know the table ID and variable names
- Example: PA111: AVERAGE MONTHLY GROSS WAGES
library(pxweb)
pa111_meta <- pxweb_get(url = "https://andmed.stat.ee/api/v1/en/stat/PA111")
pa111_metaPXWEB METADATA
PA111: AVERAGE MONTHLY GROSS WAGES (SALARIES), MEDIAN, DECILES AND NUMBER OF EMPLOYEES BY ECONOMIC ACTIVITY SECTION (QUARTERLY)
variables:
[[1]] Näitaja: Indicator
[[2]] Tegevusala: Economic activity
[[3]] Vaatlusperiood: Reference periodpa111_meta$variables[[1]]$code
[1] "Näitaja"
$text
[1] "Indicator"
$values
[1] "GR_W_AVG" "NR_EMPL" "GR_W_D1" "GR_W_D2" "GR_W_D3"
[6] "GR_W_D4" "GR_W_D5" "GR_W_D6" "GR_W_D7" "GR_W_D8"
[11] "GR_W_D9" "GR_W_AVG_SM"
$valueTexts
[1] "Average monthly gross wages (salaries), euros"
[2] "Number of employees"
[3] "1st decile of monthly gross wages (salaries), euros"
[4] "2nd decile of monthly gross wages (salaries), euros"
[5] "3rd decile of monthly gross wages (salaries), euros"
[6] "4th decile of monthly gross wages (salaries), euros"
[7] "Median (5th decile) of monthly gross wages (salaries), euros"
[8] "6th decile of monthly gross wages (salaries), euros"
[9] "7th decile of monthly gross wages (salaries), euros"
[10] "8th decile of monthly gross wages (salaries), euros"
[11] "9th decile of monthly gross wages (salaries), euros"
[12] "Percentage change in average gross wages (salaries) compared to same period in previous year, %"
$elimination
[1] FALSE
$time
[1] FALSEHow to Identify Query Variables
- You need to know the variable names to build a
pxweb_get()query - Two main ways to discover them:
- Programmatically
- Use the metadata from the
pxwebobject (e.g.pa111_list) - Example fields:
Näitaja: Indicator
Tegevusala: Economic activity
Vaatlusperiood: Reference period
- Use the metadata from the
- Via the Web Portal
- Go to andmed.stat.ee
- Find your table and click:
“API query for this table” - Shows all available dimensions and codes
px_query_list1 <- list(
"Näitaja" = c("GR_W_D1","GR_W_D5", "GR_W_D7", "GR_W_D9"),
"Tegevusala" = "*",
"Vaatlusperiood" = "*"
)
pa111 <-
pxweb_get(url = "https://andmed.stat.ee/api/v1/en/stat/PA111",
query = px_query_list1)
glimpse(pa111)List of 7
$ columns :List of 4
..$ :List of 3
.. ..$ code: chr "Näitaja"
.. ..$ text: chr "Indicator"
.. ..$ type: chr "d"
..$ :List of 3
.. ..$ code: chr "Tegevusala"
.. ..$ text: chr "Economic activity"
.. ..$ type: chr "d"
..$ :List of 3
.. ..$ code: chr "Vaatlusperiood"
.. ..$ text: chr "Reference period"
.. ..$ type: chr "t"
..$ :List of 3
.. ..$ code: chr "PA111: AVERAGE MONTHLY GROSS WAGES (SALARIES), MEDIAN, DECILES AND NUMBER OF EMPLOYEES"
.. ..$ text: chr "PA111: AVERAGE MONTHLY GROSS WAGES (SALARIES), MEDIAN, DECILES AND NUMBER OF EMPLOYEES"
.. ..$ type: chr "c"
$ comments : list()
$ data :List of 1280
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
..$ :List of 2
.. ..$ key :List of 3
.. ..$ values:List of 1
.. [list output truncated]
$ metadata :List of 1
..$ :List of 3
.. ..$ updated: chr "9999-12-31T21:59:59Z"
.. ..$ label : chr "PA111: AVERAGE MONTHLY GROSS WAGES (SALARIES), MEDIAN, DECILES AND NUMBER OF EMPLOYEES by Indicator, Economic a"| __truncated__
.. ..$ source : chr "Statistics Estonia"
$ pxweb_metadata:List of 2
..$ title : chr "PA111: AVERAGE MONTHLY GROSS WAGES (SALARIES), MEDIAN, DECILES AND NUMBER OF EMPLOYEES BY ECONOMIC ACTIVITY SEC"| __truncated__
..$ variables:List of 3
.. ..$ :List of 6
.. ..$ :List of 6
.. ..$ :List of 6
..- attr(*, "class")= chr [1:2] "pxweb_metadata" "list"
$ url : chr "https://andmed.stat.ee/api/v1/en/stat/PA111"
$ time_stamp : POSIXct[1:1], format: "2025-03-28 13:41:04"
- attr(*, "class")= chr [1:2] "pxweb_data" "list"pcite <- pxweb_cite(pa111)Statistics Estonia (2025). "PA111: AVERAGE MONTHLY GROSS WAGES
(SALARIES), MEDIAN, DECILES AND NUMBER OF EMPLOYEES BY ECONOMIC
ACTIVITY SECTION (QUARTERLY)." [Data accessed 2025-03-28
13:41:04.819378 using pxweb R package 0.17.0],
<https://andmed.stat.ee/api/v1/en/stat/PA111>.
A BibTeX entry for LaTeX users is
@Misc{,
title = {PA111: AVERAGE MONTHLY GROSS WAGES (SALARIES), MEDIAN, DECILES AND NUMBER OF EMPLOYEES BY ECONOMIC ACTIVITY SECTION (QUARTERLY)},
author = {{Statistics Estonia}},
organization = {Statistics Estonia},
address = {Tallinn, Estonia},
year = {2025},
url = {https://andmed.stat.ee/api/v1/en/stat/PA111},
note = {[Data accessed 2025-03-28 13:41:04.819378 using pxweb R package 0.17.0]},
}
Kindly cite the pxweb R package as follows:
Mans Magnusson, Markus Kainu, Janne Huovari, and Leo Lahti
(rOpenGov). pxweb: R tools for PXWEB API. URL:
http://github.com/ropengov/pxweb
A BibTeX entry for LaTeX users is
@Misc{,
title = {pxweb: R tools for PX-WEB API},
author = {Mans Magnusson and Markus Kainu and Janne Huovari and Leo Lahti},
year = {2019},
}pa111.df <- as.data.frame(pa111, column.name.type = "text", variable.value.type = "text")
pa111_code.df <- as.data.frame(pa111, column.name.type = "code", variable.value.type = "code")lapply(pa111.df, unique,2)$Indicator
[1] "1st decile of monthly gross wages (salaries), euros"
[2] "Median (5th decile) of monthly gross wages (salaries), euros"
[3] "7th decile of monthly gross wages (salaries), euros"
[4] "9th decile of monthly gross wages (salaries), euros"
$`Economic activity`
[1] "Total - all activities"
[2] "Agriculture, forestry and fishing"
[3] "Mining and quarrying"
[4] "Manufacturing"
[5] "Electricity, gas, steam and air conditioning supply"
[6] "Water supply; sewerage, waste management and remediation activities"
[7] "Construction"
[8] "Wholesale and retail trade; repair of motor vehicles and motorcycles"
[9] "Transportation and storage"
[10] "Accommodation and food service activities"
[11] "Information and communication"
[12] "Financial and insurance activities"
[13] "Real estate activities"
[14] "Professional, scientific and technical activities"
[15] "Administrative and support service activities"
[16] "Public administration and defence; compulsory social security"
[17] "Education"
[18] "Human health and social work activities"
[19] "Arts, entertainment and recreation"
[20] "Other service activities"
$`Reference period`
[1] "2021 Q1" "2021 Q2" "2021 Q3" "2021 Q4" "2022 Q1" "2022 Q2" "2022 Q3"
[8] "2022 Q4" "2023 Q1" "2023 Q2" "2023 Q3" "2023 Q4" "2024 Q1" "2024 Q2"
[15] "2024 Q3" "2024 Q4"
$`PA111: AVERAGE MONTHLY GROSS WAGES (SALARIES), MEDIAN, DECILES AND NUMBER OF EMPLOYEES`
[1] 532 472 508 560 563 609 600 651 659 700 668 724 730 771
[15] 735 796 500 452 411 522 520 543 536 597 599 595 582 654
[29] 640 680 727 666 664 778 701 786 942 804 781 850 1036 888
[43] 864 1000 1090 557 564 584 586 660 688 693 737 761 776 820
[57] 840 855 868 831 891 964 960 1008 1088 1207 1139 1191 1128 1305
[71] 1206 1303 589 567 674 713 774 760 788 829 830 459 444 495
[85] 497 487 548 577 554 580 612 623 573 605 633 650 526 438
[99] 514 510 546 581 614 615 657 675 690 683 752 762 755 553
[113] 540 636 718 725 736 780 797 799 359 200 326 383 385 462
[127] 499 455 482 498 535 569 883 851 881 907 947 1035 1094 1100
[141] 1143 1126 1169 1200 1216 1204 1247 1044 1066 1147 1326 1411 1345 1370
[155] 1444 1513 1542 1570 1621 207 205 216 223 237 249 250 267 271
[169] 288 300 307 318 574 765 800 812 400 341 380 434 465 448
[183] 490 489 519 486 512 550 576 558 579 723 739 927 871 972
[197] 904 1107 1181 1210 1089 1284 1231 1293 1290 606 631 679 733 782
[211] 828 880 622 909 616 632 665 649 728 738 759 753 825 889
[225] 991 502 317 320 398 687 689 414 731 324 277 290 363 372
[239] 375 415 426 453 436 451 471 456 475 1158 1199 1270 1240 1354
[253] 1349 1429 1424 1524 1500 1578 1553 1641 1620 1699 1001 1037 1097 1078
[267] 1159 1192 1258 1297 1341 1300 1357 1400 1462 1459 1560 1639 1690 1724
[281] 1836 1990 1862 1920 2045 1896 2050 2110 2268 1118 1220 1265 1194 1358
[295] 1363 1422 1342 1483 1487 1534 1465 1587 1589 1651 1588 1766 1693 1695
[309] 1821 1958 1900 1915 2066 2328 2100 2164 2125 2614 2253 2334 1311 1324
[323] 1365 1317 1368 1436 1527 1488 1547 1616 1683 1662 1661 1722 1816 1033
[337] 1081 1144 1167 1257 1286 1215 1312 1360 1292 1372 1399 1430 998 1054
[351] 1085 1168 1202 1222 1307 1310 1379 1391 1080 1109 1318 1415 1412 1439
[365] 1506 1525 1540 704 511 803 837 965 966 984 1074 1056 1077 1123
[379] 1149 1134 2200 2295 2350 2467 2500 2617 2661 2768 2728 2812 2873 2955
[393] 2996 3100 2000 2095 1926 1924 2127 2216 2090 2227 2395 2450 2353 2400
[407] 2555 2682 2594 2650 667 703 720 766 790 805 813 867 893 913
[421] 917 967 980 1395 1431 1471 1522 1520 1610 1654 1756 1749 1833 1840
[435] 1913 1905 1978 2069 921 896 970 990 1014 1102 1120 1129 1197 1177
[449] 1236 1313 1301 1612 1675 1812 1789 1875 2067 2135 2303 2300 2283 2443
[463] 1350 1205 1406 1463 1546 1637 1780 1602 1850 1782 1909 1726 1960 1445
[477] 1574 1564 1617 1562 1783 1820 1886 1841 1991 2017 2079 996 995 1140
[491] 1083 1250 1280 1333 1239 1408 1374 1476 779 842 892 934 962 1027
[505] 1029 1043 1556 1623 1688 1845 1827 1944 1933 2080 2028 2151 2238 2330
[519] 1427 1477 1448 1549 1627 1720 1763 1746 1861 1765 1846 1987 1934 2091
[533] 2177 2207 1965 2254 2360 2535 2413 2412 2580 2414 2585 2854 1466 1595
[547] 1596 1656 1778 1856 1760 1922 1992 1908 2064 2059 2150 2133 2481 2196
[561] 2206 2764 2509 2731 3181 2675 2799 2800 3556 2863 2949 1652 1657 1714
[575] 1685 1745 1810 1919 1891 1973 2029 2115 2106 2086 2179 2247 1481 1634
[589] 1702 1663 1794 1823 1898 1997 1380 1460 1614 1650 1773 1822 1806 1894
[603] 1901 1948 1409 1470 1531 1686 1741 1740 1864 1895 2013 2009 2049 711
[617] 1061 1188 1256 1276 1335 1393 1373 1435 1453 2988 3116 3093 3227 3309
[631] 3423 3454 3695 3712 3864 3806 3900 4000 4100 4126 4256 2692 2809 2604
[645] 2900 2995 2817 3194 3250 3117 3207 3450 3525 3401 3506 1016 1022 1071
[659] 1113 1172 1242 1273 1320 1403 1450 1510 1983 2092 2205 2274 2407 2605
[673] 2593 2687 2700 2867 1291 1447 1509 1550 1606 1710 1700 1694 1743 1807
[687] 1839 2323 2333 2613 2663 2608 2850 2834 3006 1541 1718 1491 1727 1622
[701] 1857 1892 1957 2230 1963 2272 2118 2408 2142 2430 1798 2020 1951 2062
[715] 1972 2163 2224 2474 2513 2590 2515 2750 2792 2876 1150 1461 1472 1583
[729] 1677 1613 1050 1114 1272 1308 1316 1433 1441 1508 1521 2521 2707 2600
[743] 2767 2776 3000 2940 3126 3136 3395 3238 3431 3417 3625 3500 3745 2174
[757] 2237 2342 2456 2559 2689 2609 2899 2639 2832 2980 2760 3067 2655 2944
[771] 2979 2984 2713 3046 3198 3370 3005 3347 3513 3514 3524 3755 2217 2369
[785] 2457 2627 2632 2734 2666 2926 2839 3092 3049 3201 3200 4020 3196 3633
[799] 5035 3667 4067 5548 3904 4182 4248 5605 4122 4200 2371 2349 2347 2441
[813] 2451 2601 2714 2716 2811 2856 2969 3044 3023 3231 2202 2401 2404 2532
[827] 2366 2636 2781 2637 3034 2852 3038 3265 2239 2375 2464 2640 2662 2770
[841] 2751 2860 2848 2950 2908 3030 3015 3176 2171 2290 2273 2325 2544 2568
[855] 2932 2808 2922 3074 3025 3148 1407 1538 1625 1777 1893 1911 1989 2055
[869] 2030 2221 2139 4557 4833 4790 4865 5098 5242 5286 5506 5610 5723 5697
[883] 5729 5990 6100 6169 6252 4266 4450 4061 4180 4709 4767 4475 4672 5060
[897] 5043 4800 5000 5382 5346 5100 5403 1945 2003 2005 2078 2159 2310 2429
[911] 2484 2721 2685 2864 2814 2998 3058 3159 3314 3464 3549 3604 3843 3861
[925] 3943 3933 4092 4197 4284 4317 4598 2204 2293 2345 2483 2583 2679 3027
[939] 3031 3167 3209 3291 2951 3419 3225 3338 3263 3773 3730 3578 4072 4015
[953] 3824 3833 4300 2071 2576 2039 2365 2212 2264 2621 2629 3277 3147 2902
[967] 3550 3435 3460 3266 3468 3377 3717 3672 3813 3685 4232 4254 4413 4318
[981] 4732 4728 4888 1824 1874 2075 2015 2312 2502 2753 1800 1883 1968 2072
[995] 2111 2287 2336 2488 2417 2512 2506 2683# Convert quarter to a Date object for plotting
pa111_clean <- pa111.df %>%
mutate(
quarter = yq(str_replace(`Reference period`, " ", "-"))
) %>%
filter(Indicator %in% c(
"1st decile of monthly gross wages (salaries), euros",
"Median (5th decile) of monthly gross wages (salaries), euros",
"9th decile of monthly gross wages (salaries), euros"
))
# Optional: select a few key sectors
selected_sectors <- c("Manufacturing", "Information and communication", "Financial and insurance activities")
pa111_plotdata <- pa111_clean %>%
filter(`Economic activity` %in% selected_sectors) %>%
mutate(Indicator = recode(Indicator,
"1st decile of monthly gross wages (salaries), euros" = "D1",
"Median (5th decile) of monthly gross wages (salaries), euros" = "Median",
"9th decile of monthly gross wages (salaries), euros" = "D9"
)) %>%
pivot_wider(
names_from = Indicator,
values_from = `PA111: AVERAGE MONTHLY GROSS WAGES (SALARIES), MEDIAN, DECILES AND NUMBER OF EMPLOYEES`
)
# Create the ribbon plot
ggplot(pa111_plotdata, aes(x = quarter)) +
geom_ribbon(aes(ymin = D1, ymax = D9), fill = "skyblue", alpha = 0.5) +
geom_line(aes(y = Median), color = "darkblue", linewidth = 1) +
facet_wrap(~`Economic activity`) +
labs(
title = "Wage Distribution by Sector",
subtitle = "1st to 9th Decile with Median Line (PA111)",
x = "Quarter",
y = "Gross Monthly Wages (EUR)"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Advanced topics
rdb: bare-bones interface to DBnomics- No metadata information
Manually store metadata alongside the data ::: {.fragment}
# Define query parameters
provider <- "ECB"
dataset <- "MIR"
series <- "M.EE.B.A2C.A.R.A.2250.EUR.N"
# Fetch data
mir <- rdb(provider, dataset, series)
# Manually attach metadata
attr(mir, "provider") <- provider
attr(mir, "dataset") <- dataset
attr(mir, "series") <- series
attr(mir, "downloaded_at") <- Sys.time()
attr(mir, "source_url") <- paste0("https://api.db.nomics.world/v22/series/", provider, "/", dataset, "/", series)
# Now you can inspect it later
str(attributes(mir))List of 9
$ names : chr [1:31] "@frequency" "Amount category" "AMOUNT_CAT" "Balance sheet item" ...
$ class : chr [1:2] "data.table" "data.frame"
$ row.names : int [1:299] 1 2 3 4 5 6 7 8 9 10 ...
$ .internal.selfref:<externalptr>
$ provider : chr "ECB"
$ dataset : chr "MIR"
$ series : chr "M.EE.B.A2C.A.R.A.2250.EUR.N"
$ downloaded_at : POSIXct[1:1], format: "2025-03-28 13:41:05"
$ source_url : chr "https://api.db.nomics.world/v22/series/ECB/MIR/M.EE.B.A2C.A.R.A.2250.EUR.N":::
meta <- list(
provider = provider,
dataset = dataset,
series = series,
downloaded_at = Sys.time()
)get_rdb_bibentry <- function(provider, dataset, series, format = "biblatex") {
# Required packages
if (!requireNamespace("RefManageR", quietly = TRUE)) {
stop("Package 'RefManageR' is required.")
}
if (!requireNamespace("httr", quietly = TRUE) ||
!requireNamespace("jsonlite", quietly = TRUE)) {
stop("Packages 'httr' and 'jsonlite' are required.")
}
# Build metadata URL
series_url <- paste0("https://api.db.nomics.world/v22/series/", provider, "/", dataset, "/", series)
# Fetch metadata
response <- httr::GET(series_url)
stopifnot(httr::status_code(response) == 200)
json_text <- httr::content(response, as = "text", encoding = "UTF-8")
meta <- jsonlite::fromJSON(json_text)$series$docs
# Extract fields
title <- meta$title
last_update <- as.Date(meta$updated_at)
year <- format(Sys.Date(), "%Y")
urldate <- format(Sys.Date(), "%Y-%m-%d")
citation_key <- gsub("_", "-", paste0(dataset, "-", urldate))
# Create BibEntry object
entry <- RefManageR::BibEntry(
bibtype = "misc",
key = citation_key,
title = paste0(title, " (", series, ")"),
url = series_url,
language = "english",
year = year,
author = provider,
urldate = urldate,
type = "Dataset",
note = paste0("Accessed ", urldate, ", series last updated ", last_update, ".")
)
if (format == "bibtex") {
return(utils::toBibtex(entry))
} else if (format == "biblatex") {
return(RefManageR::toBiblatex(entry))
} else {
return(entry)
}
}cat(get_rdb_bibentry(
provider = "ECB",
dataset = "MIR",
series = "M.EE.B.A2C.A.R.A.2250.EUR.N"
))@Misc{MIR-2025-03-28, title = { (M.EE.B.A2C.A.R.A.2250.EUR.N)}, url = {https://api.db.nomics.world/v22/series/ECB/MIR/M.EE.B.A2C.A.R.A.2250.EUR.N}, language = {english}, year = {2025}, author = {{ECB}}, urldate = {2025-03-28}, type = {Dataset}, note = {Accessed 2025-03-28, series last updated .}, }